Multi‐Agent‐Network‐Based Idea Generator for Zinc‐Ion Battery Electrolyte Discovery: A Case Study on Zinc Tetrafluoroborate Hydrate‐Based Deep Eutectic Electrolytes

Abstract

Abstract Aqueous deep eutectic electrolytes (DEEs) offer great potential for low‐cost zinc‐ion batteries but often have limited performance. Discovering new electrolytes is therefore crucial, yet time‐consuming and resource‐intensive. In response, this work presents a Large Language Model (LLM)‐based multi‐agent network that proposes DEE compositions for zinc‐ion batteries. By analyzing academic papers from the DEE field, the network identifies innovative, inexpensive, and sustainable Lewis bases to pair with Zn(BF 4 ) 2 ·xH 2 O. A Zn(BF 4 ) 2 ·xH 2 O‐ethylene carbonate (EC) system demonstrates high conductivity (10.6 mS cm −1 ) and a wide electrochemical stability window (2.37 V). The optimized electrolyte enables stable zinc stripping/plating, achieves outstanding rate performance (81 mAh g −1 at 5 A g −1 ), and supports 4000 cycles in Zn||polyaniline cells at 3 A g −1 . Spectroscopic analyses and simulations reveal that EC coordinates to Zn 2+ , mitigating water‐induced corrosion, while a fluorine‐rich hybrid organic/inorganic solid electrolyte interphase enhances stability. This work showcases a pioneering LLM‐driven approach to electrolyte development, establishing a new paradigm in materials research.